Integrating Generative AI with Business Intelligence: Design, Implementation, and Lessons from Baidu's ChatBI Platform

In Baidu's 115th technical salon, the article examines how generative AI can be combined with business intelligence (BI) to create intelligent BI solutions, presenting three perspectives: the technical trend and business demand, system design of the ChatBI platform, and practical challenges and solutions.

From a technical viewpoint, BI has evolved through three stages—report‑centric, self‑service, and now intelligent BI—where large language models enable natural‑language queries, reducing cost and expanding accessibility.

From a business perspective, AI‑driven BI lowers the entry barrier for non‑technical users, improves efficiency, and offers personalized data insights, with examples such as NL2SQL, AI‑generated reports, and multi‑dimensional anomaly attribution.

The ChatBI platform is introduced, highlighting its design goals: natural‑language data retrieval, rapid interaction, and accurate results. Key challenges include NL2SQL capability, interaction latency, and result correctness.

Implementation details cover two architectural options (BI platform downstream of NL2SQL vs. LLM‑controlled BI), end‑to‑end performance (model inference seconds, MPP query 2‑3 s), and accuracy improvements through prompt engineering and supervised fine‑tuning (SFT) on Baidu Cloud Qianfan.

Additional product‑level techniques such as table‑selection models, hallucination mitigation, suggestion prompts, structured result display, and fallback to existing charts are described to achieve near‑100 % accuracy.

Deployment results show hundreds of users across business lines, high satisfaction, reduced learning cost, and faster report generation compared with traditional drag‑and‑drop tools.